Tensorflow/Keras - 在一个循环中构建多个具有相同层名称的模型

Question

I want to build the same model multiple times in a for loop:

### Building the model ###

def build_model():
    # create
    model = Sequential([
        InputLayer(input_shape = (28, 28, 1)),
        Conv2D(32, (3, 3)),
        Activation('relu'),
        MaxPooling2D((2, 2)),
        Conv2D(64, (3, 3)),
        Activation('relu'),
        MaxPooling2D((2, 2)),
        Flatten(),
        Dense(num_classes),
        Activation('softmax')
    ])

    # compile
    model.compile(
        loss = 'categorical_crossentropy',
        optimizer = 'adam',
        metrics = [ 'accuracy' ]
    )

    # return
    return model


### Fit 100 models ###

for i in range(2):
    model = build_model()
    model.summary()

I get the results below.

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320       
_________________________________________________________________
activation (Activation)      (None, 26, 26, 32)        0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     
_________________________________________________________________
..........
_________________________________________________________________

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_2 (Conv2D)            (None, 26, 26, 32)        320       
_________________________________________________________________
activation_3 (Activation)    (None, 26, 26, 32)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 11, 11, 64)        18496     
_________________________________________________________________
..........
_________________________________________________________________

I would like to have 'conv2d' as my first Conv2D layer name and 'conv2d_1' as my second Conv2D layer name.

Is there a way I can get the same layer name / layer reference id in all my models?

Answer 1

这是使用所有keras.layers.Layer子级继承的name关键字参数的好机会：

model = Sequential([InputLayer(input_shape=(28,28,1), name="my_input"),Conv2D(32, 3, name="my_conv"),...])